Chat using Functions with OpenAI (ChatGPT) API on New Lead Created from ClinchPad API

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New Lead Created from the ClinchPad API

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Chat using Functions with the OpenAI (ChatGPT) API

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Getting Started#

This integration creates a workflow with a ClinchPad trigger and OpenAI (ChatGPT) action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Lead Created trigger
1. Connect your ClinchPad account
2. Configure timer
3. Select a Pipeline ID
Configure the Chat using Functions action
1. Connect your OpenAI (ChatGPT) account
2. Configure alert
3. Select a Model
4. Configure Chat Input
5. Configure Functions
6. Optional- Configure Instructions
7. Optional- Select a Tool Choice
8. Optional- Configure Parallel Function Calling
9. Optional- Configure Previous Response ID
10. Optional- Select a Truncation
11. Optional- Select a Response Format
12. Optional- Configure Skip This Step
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Lead Created on ClinchPad

Description:Emit new event for each new created lead.

Version:0.0.1

Key:clinchpad-new-lead-created

View on GitHub

ClinchPad Overview#

ClinchPad is a modern CRM tailored for small teams to manage sales pipelines with minimal fuss. The ClinchPad API integrates with Pipedream, allowing you to automate interactions with your sales pipeline. For instance, you can automatically create leads, update their status, track communications, or gather analytics for reporting purposes. Through Pipedream, you can connect ClinchPad with hundreds of other apps to streamline your sales process, maintain customer relations, and close deals faster.

Trigger Code#

import clinchpad from "../../clinchpad.app.mjs";
import { DEFAULT_POLLING_SOURCE_TIMER_INTERVAL } from "@pipedream/platform";

export default {
  name: "New Lead Created ",
  version: "0.0.1",
  key: "clinchpad-new-lead-created",
  description: "Emit new event for each new created lead.",
  type: "source",
  dedupe: "unique",
  props: {
    clinchpad,
    db: "$.service.db",
    timer: {
      type: "$.interface.timer",
      static: {
        intervalSeconds: DEFAULT_POLLING_SOURCE_TIMER_INTERVAL,
      },
    },
    pipelineId: {
      description: "The pipeline ID of the pipeline to which the leads belong to",
      propDefinition: [
        clinchpad,
        "pipelineId",
      ],
    },
  },
  methods: {
    emitEvent(data) {
      this.$emit(data, {
        id: data._id,
        summary: `New lead created with ID ${data._id}`,
        ts: new Date(),
      });
    },
  },
  hooks: {
    async deploy() {
      const leads = await this.clinchpad.getLeads({
        params: {
          pipeline_id: this.pipelineId,
          size: 10,
        },
      });

      leads.reverse().forEach(this.emitEvent);
    },
  },
  async run() {
    let page = 1;

    while (true) {
      const leads = await this.clinchpad.getLeads({
        params: {
          pipeline_id: this.pipelineId,
          page,
          size: 100,
        },
      });

      leads.reverse().forEach(this.emitEvent);

      if (leads.length < 100) {
        break;
      }

      page++;
    }
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
ClinchPad	`clinchpad`	`app`	This component uses the ClinchPad app.
N/A	`db`	`$.service.db`	This component uses `$.service.db` to maintain state between executions.
	`timer`	`$.interface.timer`
Pipeline ID	`pipelineId`	`string`	Select a value from the drop down menu.

Trigger Authentication#

ClinchPad uses API keys for authentication. When you connect your ClinchPad account, Pipedream securely stores the keys so you can easily authenticate to ClinchPad APIs in both code and no-code steps.

You authenticate to the ClinchPad API by providing your API keys in the request. You can get your API key from the Settings section of the ClinchPad web app.

About ClinchPad#

ClinchPad is a deal focused CRM that enables you to close deals faster. It is a simple to use, web based application built for small teams who are looking to transition from a cluster of spreadsheets to a more organized way of tracking leads.

Action#

Chat using Functions on OpenAI (ChatGPT)

Description:Chat with your models and allow them to invoke functions. Optionally, you can build and invoke workflows as functions. [See the documentation](https://platform.openai.com/docs/guides/function-calling)

Version:0.0.9

Key:openai-chat-using-functions

View on GitHub

OpenAI (ChatGPT) Overview#

OpenAI provides a suite of powerful AI models through its API, enabling developers to integrate advanced natural language processing and generative capabilities into their applications. Here’s an overview of the services offered by OpenAI's API:

Use Python or Node.js code to make fully authenticated API requests with your OpenAI account:

Action Code#

import openai from "../../openai.app.mjs";
import common from "../common/common.mjs";
import constants from "../../common/constants.mjs";

export default {
  ...common,
  name: "Chat using Functions",
  version: "0.0.9",
  annotations: {
    destructiveHint: false,
    openWorldHint: true,
    readOnlyHint: false,
  },
  key: "openai-chat-using-functions",
  description: "Chat with your models and allow them to invoke functions. Optionally, you can build and invoke workflows as functions. [See the documentation](https://platform.openai.com/docs/guides/function-calling)",
  type: "action",
  props: {
    openai,
    alert: {
      type: "alert",
      alertType: "info",
      content: "Provide function names and parameters, and the model will either answer the question directly or decide to invoke one of the functions, returning a function call that adheres to your specified schema. Add a custom code step that includes all available functions which can be invoked based on the model's response - [you can even build an entire workflow as a function](https://pipedream.com/docs/workflows/building-workflows/code/nodejs/#invoke-another-workflow)! Once the appropriate function or workflow is executed, continue the overall execution or pass the result back to the model for further analysis. For more details, [see this guide](https://platform.openai.com/docs/guides/function-calling?api-mode=responses#overview) and this [walkthrough](https://pipedream.com/blog/introducing-enhanced-openai-chat-actions-for-pipedream/#using-pipedream-workflows-as-functions).",
    },
    modelId: {
      propDefinition: [
        openai,
        "chatCompletionModelId",
      ],
    },
    input: {
      type: "string",
      label: "Chat Input",
      description: "Text inputs to the model used to generate a response",
    },
    functions: {
      type: "string",
      label: "Functions",
      description: "A valid JSON array of functions using OpenAI's function schema definition. [See guide here](https://platform.openai.com/docs/guides/function-calling?api-mode=responses&example=search-knowledge-base#defining-functions).",
      default:
`[
  {
    "type": "function",
    "name": "your_function_name",
    "description": "Details on when and how to use the function",
    "strict": true,
    "parameters": {
      "type": "object",
      "properties": {
        "property_name": {
          "type": "property_type",
          "description": "A description for this property"
        },
        "another_property_name": {
          "type": "property_type",
          "description": "A description for this property"
        }
      },
      "required": [
        "list",
        "of",
        "required",
        "properties",
        "for",
        "this",
        "object"
      ],
      "additionalProperties": false
    }
  }
]`,
    },
    instructions: {
      type: "string",
      label: "Instructions",
      description: "Inserts a system (or developer) message as the first item in the model's context",
      optional: true,
    },
    toolChoice: {
      type: "string",
      label: "Tool Choice",
      description: "- **auto**: The model decides whether and how many functions to call.\n- **required**: The model must call one or more functions.\n- **function_name**: Enter a custom expression to force the model to call this specific function.",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "required",
      ],
    },
    parallelToolCalls: {
      type: "boolean",
      label: "Parallel Function Calling",
      description: "Allow or prevent the model to call multiple functions in a single turn",
      optional: true,
      default: true,
    },
    previousResponseId: {
      type: "string",
      label: "Previous Response ID",
      description: "The unique ID of the previous response to the model. Use this to create multi-turn conversations",
      optional: true,
    },
    truncation: {
      type: "string",
      label: "Truncation",
      description: "Specifies the truncation mode for the response if it's larger than the context window size",
      optional: true,
      default: "auto",
      options: [
        "auto",
        "disabled",
      ],
    },
    responseFormat: {
      type: "string",
      label: "Response Format",
      description: "- **Text**: Returns unstructured text output.\n- **JSON Schema**: Enables you to define a [specific structure for the model's output using a JSON schema](https://platform.openai.com/docs/guides/structured-outputs?api-mode=responses).",
      options: [
        "text",
        "json_schema",
      ],
      default: "text",
      optional: true,
      reloadProps: true,
    },
    skipThisStep: {
      type: "boolean",
      label: "Skip This Step",
      description: "Pass in a boolean custom expression to skip this step's execution at runtime",
      optional: true,
      default: false,
    },
  },
  additionalProps() {
    const {
      modelId,
      responseFormat,
    } = this;
    const props = {};

    if (this.openai.isReasoningModel(modelId)) {
      props.reasoningEffort = {
        type: "string",
        label: "Reasoning Effort",
        description: "Constrains effort on reasoning for reasoning models",
        optional: true,
        options: [
          "low",
          "medium",
          "high",
        ],
      };

      // aparrently not supported yet as of 12/march/2025
      // props.generateSummary = {
      //   type: "string",
      //   label: "Generate Reasoning Summary",
      //   description: "A summary of the reasoning performed by the model",
      //   optional: true,
      //   options: [
      //     "concise",
      //     "detailed",
      //   ],
      // };
    }

    if (responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      props.jsonSchema = {
        type: "string",
        label: "JSON Schema",
        description: "Define the schema that the model's output must adhere to. [Generate one here](https://platform.openai.com/docs/guides/structured-outputs/supported-schemas).",
      };
    }

    return props;
  },
  methods: {
    ...common.methods,
  },
  async run({ $ }) {
    if (this.skipThisStep) {
      $.export("$summary", "Step execution skipped");
      return;
    }

    const data = {
      model: this.modelId,
      input: this.input,
      instructions: this.instructions,
      previous_response_id: this.previousResponseId,
      truncation: this.truncation,
      parallel_tool_calls: this.parallelToolCalls,
      tools: [],
    };

    let functions = this.functions;
    if (typeof functions === "string") {
      try {
        functions = JSON.parse(functions);
      } catch (error) {
        throw new Error("Invalid JSON format in the provided Functions Schema");
      }
    }

    if (Array.isArray(functions)) {
      data.tools.push(...functions);
    } else {
      data.tools.push(functions);
    }

    if (this.toolChoice) {
      if (this.toolChoice === "auto" || this.toolChoice === "required") {
        data.tool_choice = this.toolChoice;
      } else {
        data.tool_choice = {
          type: "function",
          name: this.toolChoice,
        };
      }
    }

    if (this.openai.isReasoningModel(this.modelId) && this.reasoningEffort) {
      data.reasoning = {
        ...data.reasoning,
        effort: this.reasoningEffort,
      };
    }

    if (this.openai.isReasoningModel(this.modelId) && this.generateSummary) {
      data.reasoning = {
        ...data.reasoning,
        generate_summary: this.generateSummary,
      };
    }

    if (this.responseFormat === constants.CHAT_RESPONSE_FORMAT.JSON_SCHEMA.value) {
      try {
        data.text = {
          format: {
            type: this.responseFormat,
            ...JSON.parse(this.jsonSchema),
          },
        };
      } catch (error) {
        throw new Error("Invalid JSON format in the provided JSON Schema");
      }
    }

    const response = await this.openai.responses({
      $,
      data,
    });

    if (response) {
      $.export("$summary", `Successfully sent chat with id ${response.id}`);
      $.export("chat_responses", response.output);
    }

    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
OpenAI (ChatGPT)	`openai`	`app`	This component uses the OpenAI (ChatGPT) app.
Model	`modelId`	`string`	Select a value from the drop down menu.
Chat Input	`input`	`string`	Text inputs to the model used to generate a response
Functions	`functions`	`string`	A valid JSON array of functions using OpenAI's function schema definition. See guide here
Instructions	`instructions`	`string`	Inserts a system (or developer) message as the first item in the model's context
Tool Choice	`toolChoice`	`string`	Select a value from the drop down menu:`autorequired`
Parallel Function Calling	`parallelToolCalls`	`boolean`	Allow or prevent the model to call multiple functions in a single turn
Previous Response ID	`previousResponseId`	`string`	The unique ID of the previous response to the model. Use this to create multi-turn conversations
Truncation	`truncation`	`string`	Select a value from the drop down menu:`autodisabled`
Response Format	`responseFormat`	`string`	Select a value from the drop down menu:`textjson_schema`
Skip This Step	`skipThisStep`	`boolean`	Pass in a boolean custom expression to skip this step's execution at runtime

Chat using Functions with OpenAI (ChatGPT) API on New Lead Created from ClinchPad API

Pipedream makes it easy to connect APIs for OpenAI (ChatGPT), ClinchPad and 2,900+ other apps remarkably fast.

Trusted by 1,000,000+ developers from startups to Fortune 500 companies

Developers ♥ Pipedream

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